The majority of people can instantly tell when writing is generated by AI. For those who don't intend to get into the weeds about the data, the most obvious tell is the overused em dash (of course). Right behind that are flaws that software cannot easily scan. AI writing has a flat, predictable sentence rhythm and a constant, unnatural positivity. The paragraphs look polished but say nothing. This makes AI detection incredibly difficult. The signs that human readers trust the most are unfortunately the exact ones that software cannot measure.

**Methodology:**

I pulled the Arctic Shift Reddit archive: 89,239 posts across 47 subreddits (r/ChatGPT, r/WritingWithAI, r/SaaS, r/aiwars, r/ClaudeAI, r/Professors, r/Teachers, and the rest), 2021 to 2026. After filtering to posts that are actually about spotting AI writing, 7,984 were on-topic, split across three lanes: AI tools, writing, and SaaS. Every figure below is a share of those on-topic posts, not a raw count, because the topic barely existed before 2023 (26 on-topic posts in 2021, 86 in 2022) and then exploded (587 in 2023, 3,174 in 2025), so raw counts mostly track the subreddits growing.

It is important to note that a keyword pass badly miscounts this topic, so I hand-audited a 600-post sample to record what people actually *cite* as a tell, versus what a pattern merely matches.

Why does all AI writing converge on the same voice? Every model is tuned for a safe and agreeable register that reads as "good writing" to a grader, so everyone's default lands in the same place. One commenter put the effect plainly: "ChatGPT has a very recognizable cadence. And as soon as you catch it, it is impossible to focus on what's being written, because it's not even someone's actual thoughts." (r/ChatGPT)

**The tells, ranked by how often people actually cite them:**

|Rank|Tell|What people say|
|:-|:-|:-|
|1|The em dash (cited in 7.1% of audited posts, the top tell by a wide margin).|"Em dashes have become the single most reliable tell of AI-generated text." (r/ChatGPT)|
|2|A flat, uniform sentence rhythm (cited 4.0%, and no scanner can see it).|"Every YouTube video script I watch has the same cadence, the same verbiage, the same fucking chatGPT slop." (r/ChatGPT)|
|3|The "not just X, it's Y" cadence (cited 2.8%, the top sentence-level tell).|People list it right next to the punctuation: "even beyond the obvious em dashes and 'not just x, it's y'." (r/ChatGPT)|
|4|The five-paragraph shape and the "in conclusion" wrap-up (cited 2.5%).|They "leave in those super obvious lines like 'In conclusion, this essay has discussed...'." (r/ChatGPT)|
|5|The diction memes: "delve," "leverage," "seamless," "tapestry" (cited 1.3% as a cluster).|A prompt people pass around to fix it: "no telltale signs like em dashes, overused words like 'seamless'." (r/ChatGPT)|
|6|Leftover assistant boilerplate, the "as an AI language model" line (cited 1.2%).|The other line people forget to delete: "As an AI developed by OpenAI...". (r/ChatGPT)|
|7|The hollow scene-setting opener (cited 0.7%, low but iconic).|A whole post written in the voice, quoted as the example: "I wanted to take a moment to delve into something that's been on my mind lately. In today's fast-paced digital landscape..." (r/ClaudeAI)|

Two tells belong in the top five but are missing from that table on purpose, because no keyword can catch them and the audited readers named them anyway. Sycophancy (the "great question!" opener, the reflexive refusal to take a side) is cited about as often as the antithesis cadence. So is saying nothing at length (i.e., prose that is grammatical and confident but makes no actual claim). A pattern-matcher is blind to both of those things so I could not check for them when I scanned for data, but they are obviously very real.

It's important to note some corrections that resulted from me auditing the data myself. A naive keyword scanner gets this topic backwards in two ways. First, it massively over-counts ordinary words. "however," "thus," and "hence" are the single highest keyword match in the corpus at 6.3% of posts, and they're cited as a tell 0% of the time, because they're just people writing normally. The same is true for "nuanced," "comprehensive," "when it comes to," and "utilize." If you build a detector on a word list, this is most of what it flags, and it's nearly all false. Second, it under-counts or entirely misses the tells that rank highest with real readers, the flat rhythm and the fluent-but-empty paragraph, because no word list can see them. The lesson is that the cheap signal and the real signal point in different directions, which is exactly why the cited column, not the keyword column, drives the ranking above.

There is a fair counterpoint that came up enough to belong here, which is that none of this is strictly an AI problem. The em dash is good typography. Formal diction and a tidy structure are how a lot of careful people, students and non-native English speakers especially, have always written. So these tells absolutely predate AI. What (unfortunately) changed is that AI made everyone produce them at once, so the people who always wrote this way are the ones getting flagged. One teacher's post is titled "My students discovered AI checkers and are now terrified of their own writing." (r/Teachers) Another writer leads with "English is not my first language. I wrote this in Chinese and translated it with AI help. The writing may have some AI flavor," and then makes a sharp original argument anyway. (r/LocalLLaMA)

As many of us have experienced, every item on the list is the model's default reach when you don't specify otherwise. Cut the em dash. Say the thing plainly instead of negating it first. Vary your sentence length so the rhythm isn't a metronome. Drop the flattery and take a position. Use contractions. Let the structure follow the argument instead of the intro-body-conclusion mold. The fix that showed up most often in the data was simply to stop letting the model pick the voice. Give it a real sample of how you write and then read the result out loud, because the rhythm is the tell your ear catches before your eye does.

**Thirteen graphs are attached, with the underlying tables:**

1. **The cited ranking:** each tell by how often audited posts name it. The em dash leads, and the structural tells a scanner can't see sit right behind it.
2. **Cited versus keyword-matched:** the same tells under both signals, showing where a word list inflates a tell ("however," "nuanced") and where it misses one ("as an AI," the structural ones).
3. **The keyword ranking:** the broad lexicon pass over all 7,984 on-topic posts, the noisier secondary view.
4. **Growth over time:** talk of AI-writing tells as a share of posts pulled each year, near nothing before 2023.
5. **Tell trend by year:** the top tells over time. The em dash is essentially absent before 2024 and then jumps, the cleanest before-and-after in the data.
6. **Scale and coverage:** posts pulled from each subreddit, 89,239 in total.
7. **Raw counts per tell:** the actual post counts behind the percentages.
8. **The funnel:** how 89,239 pulled posts narrow to 7,984 on-topic and a 600-post audited core.
9. **Concentration points:** on-topic posts as a share of each sub's own volume. r/WritingWithAI runs near a third of its posts.
10. **Co-occurrence:** which tells get named together in the same post.
11. **Tells by family:** diction words versus sentence phrasing versus formatting versus pasted assistant artifacts.
12. **Top posts:** the highest-upvoted on-topic threads the signal comes from.
13. **Lens 2:** for the specific terms I queried directly, how much of their airtime lands in an AI-writing context, and across how many subreddits.

(!) This is what vocal, online people say, so trust the ordering more than the exact percentages. Keyword matching can catch the wrong sense of a word or miss sarcasm, which is why the generic-word counts run high and why I audited a sample by hand. The relative order is the thing to take away, not the decimal.

Full data, scripts, the scanner, and all charts are here: [https://github.com/JCarterJohnson/vibecoded-design-tells](https://github.com/JCarterJohnson/vibecoded-design-tells) (the unslop-ai-text folder). It has the pulled corpus, the tell-count tables, the 600-post audit, and the harvester, so you can rerun it against the public Arctic Shift archive yourself.

**============================================================**

This is a Part 2 post on the original post I made about vibe-coding giveaways in website UI. I'm planning on turning this into a 3-part mini research series that spans AI "tells" in ui, text, and code. Will update links progressively:

1. [AI giveaways in UI](https://www.reddit.com/r/ClaudeCode/comments/1u7g0z5/i_scanned_3200000_posts_across_47_ai_and_saas/) \-- /unslop-ai-ui skill (in [repo](https://github.com/JCarterJohnson/vibecoded-design-tells))
2. AI giveaways in text (this post) -- /unslop-ai-text skill (in [repo](https://github.com/JCarterJohnson/vibecoded-design-tells/tree/main/unslop-ai-text))
3. AI giveaways in code (...coming) -- /unslop-ai-code skill (in [repo](https://github.com/JCarterJohnson/vibecoded-design-tells/tree/main/unslop-ai-code))

I pulled ~90,000 Reddit posts about what makes writing "sound like AI" to determine the biggest AI-slop giveaways (Part 2)